CompetitiveBike: Competitive Prediction of Bike-Sharing Apps Using Heterogeneous Crowdsourced Data

TL;DR

This paper introduces CompetitiveBike, a system that predicts and compares the popularity of competing bike-sharing apps using heterogeneous crowdsourced data, addressing a gap in existing research.

Contribution

The paper presents a novel approach to forecast the popularity contest among bike-sharing apps, leveraging diverse data sources for the first time.

Findings

Effective prediction of app popularity contest demonstrated

Real-world data validates the approach

Outperforms baseline methods

Abstract

In recent years, bike-sharing systems have been deployed in many cities, which provide an economical lifestyle. With the prevalence of bike-sharing systems, a lot of companies join the market, leading to increasingly fierce competition. To be competitive, bike-sharing companies and app developers need to make strategic decisions for mobile apps development. Therefore, it is significant to predict and compare the popularity of different bike-sharing apps. However, existing works mostly focus on predicting the popularity of a single app, the popularity contest among different apps has not been explored yet. In this paper, we aim to forecast the popularity contest between Mobike and Ofo, two most popular bike-sharing apps in China. We develop CompetitiveBike, a system to predict the popularity contest among bike-sharing apps. Moreover, we conduct experiments on real-world datasets collected from 11 app stores and Sina Weibo, and the experiments demonstrate the effectiveness of our approach.